1 citations · 1 across the 2 of their papers we have counts for
5 papers
Causality-Aware End-to-End Autonomous Driving via Ego-Centric Joint Scene Modeling
Seokha Moon, Minseung Lee, Joon Seo +2
End-to-end autonomous driving, which bypasses traditional modular pipelines by directly predicting future trajectories from sensor inputs, has recently achieved substantial progres…
SUPER-AD: Semantic Uncertainty-aware Planning for End-to-End Robust Autonomous Driving
Wonjeong Ryu, Seungjun Yu, Seokha Moon +4
End-to-End (E2E) planning has become a powerful paradigm for autonomous driving, yet current systems remain fundamentally uncertainty-blind. They assume perception outputs are full…
Streaming Dense Voxel Representations for 3D Occupancy Prediction
Seokha Moon, Janghyun Baek, Yujin Jeong +5
In this paper, we explore dense voxel streaming for accurate and efficient 3D occupancy prediction. While dense voxel representations offer fine-grained spatial details and streami…
Image-Guided Semantic Pseudo-LiDAR Point Generation for 3D Object Detection
Minseung Lee, Seokha Moon, Seung Joon Lee +2
In autonomous driving scenarios, accurate perception is becoming an even more critical task for safe navigation. While LiDAR provides precise spatial data, its inherent sparsity ma…
VisionTrap: Vision-Augmented Trajectory Prediction Guided by Textual Descriptions
Seokha Moon, Hyun Woo, Hongbeen Park +6
Predicting future trajectories for other road agents is an essential task for autonomous vehicles. Established trajectory prediction methods primarily use agent tracks generated by…